docstr: predict thread
          
docstr_tok: ["predict","thread"]
code: def run_predict(self, thread_id):
      """predict thread"""
      batch_ident = []
      batch_obs = []
      while True:
          ident, obs = self.predict_input_queue.get()
  
          batch_ident.append(ident)
          batch_obs.append(obs)
          while len(batch_obs) < self.config["max_predict_batch_size"]:
              try:
                  ident, obs = self.predict_input_queue.get_nowait()
                  batch_ident.append(ident)
                  batch_obs.append(obs)
              except queue.Empty:
                  break
          if batch_obs:
              batch_obs = np.array(batch_obs)
              actions, values = self.agent.sample(batch_obs, thread_id)
  
              for i, ident in enumerate(batch_ident):
                  self.predict_output_queues[ident].put(
                      (actions[i], values[i])
                  )
              batch_ident = []
              batch_obs = []
  
imports: from parl.utils import check_version_for_fluid
  import gym
  import numpy
  import os
  from six.moves import queue
  import six
  import time
  import threading
  import parl
  from atari_model import AtariModel
  from atari_agent import AtariAgent
  from collections import defaultdict
  from parl.env.atari_wrappers import wrap_deepmind
  from parl.utils import logger, get_gpu_count, summary
  from parl.utils.scheduler import PiecewiseScheduler
  from parl.utils.time_stat import TimeStat
  from parl.utils.window_stat import WindowStat
  from parl.utils.rl_utils import calc_gae
  from parl.utils import machine_info
  from actor import Actor
label_desc: Use image node_id 3 for calling the Learner obj's underlying member method code with example usage: obj.run_predict(thread_id) without return types